Lal Hussain

2.5k total citations
88 papers, 1.6k citations indexed

About

Lal Hussain is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Lal Hussain has authored 88 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Artificial Intelligence, 20 papers in Computer Vision and Pattern Recognition and 16 papers in Cognitive Neuroscience. Recurrent topics in Lal Hussain's work include AI in cancer detection (17 papers), EEG and Brain-Computer Interfaces (16 papers) and Radiomics and Machine Learning in Medical Imaging (13 papers). Lal Hussain is often cited by papers focused on AI in cancer detection (17 papers), EEG and Brain-Computer Interfaces (16 papers) and Radiomics and Machine Learning in Medical Imaging (13 papers). Lal Hussain collaborates with scholars based in Pakistan, Saudi Arabia and United States. Lal Hussain's co-authors include Wajid Aziz, Sharjil Saeed, Imtiaz Ahmed Awan, Adeel Abbasi, Malik Sajjad Ahmed Nadeem, Kashif Javed Lone, Saima Rathore, Abdul Majid, Shahzad Ahmad Qureshi and Adnan Idris and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Lal Hussain

83 papers receiving 1.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Lal Hussain Pakistan 24 647 430 340 238 215 88 1.6k
Adi Alhudhaif Saudi Arabia 27 630 1.0× 242 0.6× 513 1.5× 152 0.6× 140 0.7× 106 2.0k
Harikumar Rajaguru India 20 815 1.3× 314 0.7× 261 0.8× 242 1.0× 550 2.6× 244 1.8k
Kaijian Xia China 19 401 0.6× 259 0.6× 399 1.2× 165 0.7× 144 0.7× 94 1.3k
Padmavathi Kora India 17 361 0.6× 170 0.4× 306 0.9× 115 0.5× 229 1.1× 68 1.4k
Venkatanareshbabu Kuppili India 21 657 1.0× 346 0.8× 154 0.5× 68 0.3× 92 0.4× 69 1.5k
Ümit Budak Türkiye 21 602 0.9× 542 1.3× 517 1.5× 158 0.7× 94 0.4× 42 1.6k
Shivajirao M. Jadhav India 15 474 0.7× 388 0.9× 240 0.7× 120 0.5× 145 0.7× 25 1.2k
Mohammad Khubeb Siddiqui Australia 13 722 1.1× 760 1.8× 252 0.7× 76 0.3× 347 1.6× 43 1.8k
Muazzam Maqsood Pakistan 29 913 1.4× 330 0.8× 456 1.3× 546 2.3× 81 0.4× 84 2.6k
Dazhe Zhao China 22 423 0.7× 643 1.5× 463 1.4× 155 0.7× 65 0.3× 146 1.5k

Countries citing papers authored by Lal Hussain

Since Specialization
Citations

This map shows the geographic impact of Lal Hussain's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lal Hussain with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lal Hussain more than expected).

Fields of papers citing papers by Lal Hussain

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lal Hussain. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lal Hussain. The network helps show where Lal Hussain may publish in the future.

Co-authorship network of co-authors of Lal Hussain

This figure shows the co-authorship network connecting the top 25 collaborators of Lal Hussain. A scholar is included among the top collaborators of Lal Hussain based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lal Hussain. Lal Hussain is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Khurshid, Muhammad, et al.. (2025). Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction. PLoS ONE. 20(1). e0310218–e0310218. 7 indexed citations
2.
Shah, Mohsin, Mushtaq Ali, Lal Hussain, et al.. (2025). A hybrid multi-panel image segmentation framework for improved medical image retrieval system. PLoS ONE. 20(2). e0315823–e0315823. 1 indexed citations
3.
Diao, Su, Shijia Huang, Touseef Sadiq, et al.. (2025). Optimizing Bi-LSTM networks for improved lung cancer detection accuracy. PLoS ONE. 20(2). e0316136–e0316136. 3 indexed citations
4.
Ali, Mushtaq, et al.. (2024). Fault tolerant & priority basis task offloading and scheduling model for IoT logistics. Alexandria Engineering Journal. 110. 400–419.
5.
Shim, Seong‐O, Lal Hussain, Wajid Aziz, et al.. (2024). Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach. International Journal of Imaging Systems and Technology. 34(2). 4 indexed citations
6.
Hussain, Lal. (2024). Fortifying AI Against Cyber Threats Advancing Resilient Systems to Combat Adversarial Attacks. SHILAP Revista de lepidopterología. 2024. 26–31. 7 indexed citations
7.
Hussain, Lal, et al.. (2023). Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection. Computers, materials & continua/Computers, materials & continua (Print). 75(3). 5213–5228. 3 indexed citations
8.
Raza, Basit, et al.. (2023). The Deep Learning ResNet101 and Ensemble XGBoost Algorithm with Hyperparameters Optimization Accurately Predict the Lung Cancer. Applied Artificial Intelligence. 37(1). 20 indexed citations
9.
Qureshi, Shahzad Ahmad, et al.. (2023). EML-PSP: A novel ensemble machine learning-based physical security paradigm using cross-domain ultra-fused feature extraction with hybrid data augmentation scheme. Expert Systems with Applications. 243. 122863–122863. 4 indexed citations
10.
Siddiqui, Ghazanfar Farooq, et al.. (2023). Country Level Social Aggression Using Computational Modelling. 3(2). 52–62.
11.
12.
Qureshi, Shahzad Ahmad, et al.. (2022). Recent Development of Fluorescent Nanodiamonds for Optical Biosensing and Disease Diagnosis. Biosensors. 12(12). 1181–1181. 34 indexed citations
13.
Ali, Mushtaq, Marwa Obayya, Junaid Asghar, et al.. (2022). Machine learning based skin lesion segmentation method with novel borders and hair removal techniques. PLoS ONE. 17(11). e0275781–e0275781. 6 indexed citations
14.
Nadeem, Malik Sajjad Ahmed, et al.. (2020). Machine Learning Based Cost Effective Electricity Load Forecasting Model Using Correlated Meteorological Parameters. IEEE Access. 8. 146847–146864. 58 indexed citations
15.
16.
Abbasi, Adeel, et al.. (2020). Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. Cognitive Neurodynamics. 14(4). 523–533. 97 indexed citations
17.
Hussain, Lal, Tony Nguyen, Haifang Li, et al.. (2020). Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection. BioMedical Engineering OnLine. 19(1). 88–88. 83 indexed citations
18.
Afridi, Muhammad Abdur Rahman, et al.. (2018). MICROALBUMINURIA AND ITS CORRELATION WITH GLYCEMIC CONTROL IN TYPE 2 DIABETIC PATIENTS. Journal of Postgraduate Medical Institute. 32(3). 2 indexed citations
19.
Hussain, Lal, Wajid Aziz, & Sharjil Saeed. (2017). Coupling Functions between Brain Waves: Significance of Opened/Closed Eyes. SHILAP Revista de lepidopterología. 1 indexed citations
20.
Hussain, Lal, Wajid Aziz, Sharjil Saeed, et al.. (2017). Complexity analysis of EEG motor movement with eye open and close subjects using multiscale permutation entropy (MPE) technique. Biomedical Research-tokyo. 28(16). 7104–7111. 23 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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